The present technology relates to imaging systems and methods for reading and decoding symbols, and more specifically, to imaging systems and methods for reading symbols within an extended reading range.
Imaging systems use image acquisition devices that include image sensors to deliver information on a viewed subject. The system then interprets this information according to a variety of algorithms to perform a programmed decision-making and/or identification function. For an image to be most effectively acquired by a sensor in the visible, and near-visible light range, the subject is typically illuminated.
Symbology reading (also commonly termed “barcode” scanning) using an image sensor, entails the aiming of an image acquisition system including optics (lenses) and a sensor (CMOS camera, CCD, etc.) at a location on an object that contains a symbol (a “barcode” for example), and acquiring an image of that symbol. The symbol contains a set of predetermined patterns that represent an ordered group of characters or shapes from which an attached data processor (for example a microcomputer) can derive useful information about the object (e.g. its serial number, type, model, price, etc.). Symbols/barcodes are available in a variety of shapes and sizes. Two of the most commonly employed symbol types used in marking and identifying objects are the so-called one-dimensional barcode, consisting of a sequence of bars and spaces of varying width, and the so-called two-dimensional barcode consisting of a two-dimensional array of dots or rectangles.
Code readers typically have a limited range in which they are able to accurately read and decode an image. There are significant challenges to overcome when attempting to broaden the reading range: obtaining a sharp image over a long range, sufficient resolution at large distances, a sufficiently large field of view at small distances, and sufficient lighting to prevent blur. Additionally, there are significant challenges associated with achieving illumination that is perceived by the user as continuous. Using a conventional image formation system makes it difficult to overcome these challenges. This is especially true for handheld devices, where the available space and electrical power are limited.
A need exists for improved systems and methods for the acquisition and decoding of symbols, and in particular, to overcome the shortcomings relating to the reading range of conventional image formation systems.